import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from prophet import Prophet
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from sklearn.metrics import mean_absolute_error
sns.set(style="whitegrid")
df_bills = pd.read_csv("bills.csv", parse_dates = ["bill_paid_at_local", "business_date"])
df_bills.head()
| bill_paid_at_local | bill_total_billed | bill_total_discount_item_level | bill_total_gratuity | bill_total_net | bill_total_tax | bill_total_voided | bill_uuid | business_date | order_duration_seconds | order_seated_at_local | order_closed_at_local | order_take_out_type_label | order_uuid | payment_amount | payment_count | payment_total_tip | sales_revenue_with_tax | venue_xref_id | waiter_uuid | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-07-01 09:17:01 | 21.81 | 0.0 | 0.0 | 19.30 | 2.51 | 0.0 | 240701091701~4D63608F-523C-4EFF-9A4F-78D6C44B5159 | 2024-07-01 | 46 | 2024-07-01 09:16:15 | 2024-07-01 09:17:01 | dinein | 240701091615~EF1C6E91-B6C4-4DF1-8A92-1B024197FEC3 | 21.81 | 1 | 0.0 | 21.81 | 39e5b4830d4d9c14db7368a95b65d5463ea3d095203737... | 161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD |
| 1 | 2024-07-01 13:37:00 | 48.95 | 0.0 | 0.0 | 43.32 | 5.63 | 0.0 | 240701133659~3A0BEDE2-E9E5-484D-B909-780E485F0D69 | 2024-07-01 | 288 | 2024-07-01 13:32:12 | 2024-07-01 13:37:00 | dinein | 240701133212~A4C33BFA-A54F-4627-B0C2-7428427FB5DE | 48.95 | 1 | 0.0 | 48.95 | 39e5b4830d4d9c14db7368a95b65d5463ea3d095203737... | 161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD |
| 2 | 2024-07-01 11:19:32 | 26.94 | 0.0 | 0.0 | 23.84 | 3.10 | 0.0 | 240701111931~670EEB85-E939-4924-B92F-C95076B7E930 | 2024-07-01 | 135 | 2024-07-01 11:17:17 | 2024-07-01 11:19:32 | dinein | 240701111717~6074B0AB-2432-484D-BC3F-55CC5D731818 | 26.94 | 1 | 0.0 | 26.94 | 39e5b4830d4d9c14db7368a95b65d5463ea3d095203737... | 161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD |
| 3 | 2024-07-01 15:05:59 | 3.96 | 0.0 | 0.0 | 3.77 | 0.19 | 0.0 | 240701150558~3D6F473E-D5EF-4BB8-AFAB-30DB2A0764C0 | 2024-07-01 | 48 | 2024-07-01 15:05:11 | 2024-07-01 15:05:59 | dinein | 240701150511~EAA68F41-20ED-4FC7-B902-B0B3878AEC82 | 3.96 | 1 | 0.0 | 3.96 | 39e5b4830d4d9c14db7368a95b65d5463ea3d095203737... | 161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD |
| 4 | 2024-07-01 12:41:00 | 3.36 | 0.0 | 0.0 | 3.20 | 0.16 | 0.0 | 240701124059~0521794C-51FC-4C30-A368-995CE4DE105F | 2024-07-01 | 103 | 2024-07-01 12:39:17 | 2024-07-01 12:41:00 | dinein | 240701123917~27764E1C-7E7F-4293-ABC7-5921310A654A | 3.36 | 1 | 0.0 | 3.36 | 39e5b4830d4d9c14db7368a95b65d5463ea3d095203737... | 161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD |
df_bills.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 8954786 entries, 0 to 8954785 Data columns (total 20 columns): # Column Dtype --- ------ ----- 0 bill_paid_at_local datetime64[ns] 1 bill_total_billed float64 2 bill_total_discount_item_level float64 3 bill_total_gratuity float64 4 bill_total_net float64 5 bill_total_tax float64 6 bill_total_voided float64 7 bill_uuid object 8 business_date datetime64[ns] 9 order_duration_seconds int64 10 order_seated_at_local object 11 order_closed_at_local object 12 order_take_out_type_label object 13 order_uuid object 14 payment_amount float64 15 payment_count int64 16 payment_total_tip float64 17 sales_revenue_with_tax float64 18 venue_xref_id object 19 waiter_uuid object dtypes: datetime64[ns](2), float64(9), int64(2), object(7) memory usage: 1.3+ GB
df_bills.describe()
| bill_paid_at_local | bill_total_billed | bill_total_discount_item_level | bill_total_gratuity | bill_total_net | bill_total_tax | bill_total_voided | business_date | order_duration_seconds | payment_amount | payment_count | payment_total_tip | sales_revenue_with_tax | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 8954786 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8954786 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 | 8.954786e+06 |
| mean | 2024-09-26 12:26:41.071778816 | 3.674831e+01 | 8.403802e-01 | 2.408956e-01 | 3.389894e+01 | 2.849375e+00 | 9.375093e-01 | 2024-09-25 20:08:50.833184768 | 8.790968e+03 | 4.063878e+01 | 9.844425e-01 | 3.654212e+00 | 3.674902e+01 |
| min | 2024-07-01 00:00:01 | -5.731500e+03 | -9.164000e+01 | -6.000000e+01 | -5.072120e+03 | -6.593800e+02 | -4.501500e+03 | 2024-07-01 00:00:00 | -4.400000e+01 | -5.731500e+03 | 0.000000e+00 | -2.531400e+02 | -5.731500e+03 |
| 25% | 2024-08-12 12:20:46.249999872 | 1.044000e+01 | 0.000000e+00 | 0.000000e+00 | 9.630000e+00 | 4.600000e-01 | 0.000000e+00 | 2024-08-12 00:00:00 | 1.100000e+02 | 1.097000e+01 | 1.000000e+00 | 0.000000e+00 | 1.044000e+01 |
| 50% | 2024-09-23 22:52:23 | 2.189000e+01 | 0.000000e+00 | 0.000000e+00 | 2.000000e+01 | 1.440000e+00 | 0.000000e+00 | 2024-09-23 00:00:00 | 1.159000e+03 | 2.347000e+01 | 1.000000e+00 | 0.000000e+00 | 2.189000e+01 |
| 75% | 2024-11-09 09:56:19.500000 | 4.465000e+01 | 0.000000e+00 | 0.000000e+00 | 4.100000e+01 | 3.450000e+00 | 0.000000e+00 | 2024-11-09 00:00:00 | 3.377000e+03 | 4.887000e+01 | 1.000000e+00 | 4.070000e+00 | 4.465000e+01 |
| max | 2025-01-01 21:41:43 | 7.492861e+04 | 5.490920e+03 | 1.193553e+04 | 6.630850e+04 | 8.620110e+03 | 1.000000e+06 | 2024-12-31 00:00:00 | 2.894124e+08 | 5.227523e+05 | 4.100000e+01 | 5.227156e+05 | 7.492861e+04 |
| std | NaN | 8.337650e+01 | 9.404221e+00 | 8.409234e+00 | 7.754356e+01 | 7.153385e+00 | 3.365966e+02 | NaN | 3.984398e+05 | 1.987292e+02 | 1.926432e-01 | 1.753186e+02 | 8.337668e+01 |
df_venues = pd.read_csv("venues.csv")
df_venues.head()
| venue_xref_id | concept | city | country | start_of_day_offset | |
|---|---|---|---|---|---|
| 0 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 |
| 1 | e7cec41c46a9706ba1a702b368be5431913b6dc9c9da49... | FAMILY_DINING | Brooklyn | US | 00:00:00 |
| 2 | e63e79791883b0c4ac71d41eaee727932c13d6a4ec8f78... | FAST_CASUAL | Windsor | CA | 00:00:00 |
| 3 | c100388c9328b30b6bdfbea43f113d367ae9d32b03e84b... | BAR | Burnaby | CA | 01:00:00 |
| 4 | a819ee7f83f27dd3d7f44515e22aca3c26e6704ddd2a9b... | FAMILY_DINING | Tisdale | CA | 00:00:00 |
new_df = pd.merge(df_venues, df_bills, on="venue_xref_id", how="inner")
print(new_df)
venue_xref_id concept \
0 ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... FAST_FOOD
1 ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... FAST_FOOD
2 ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... FAST_FOOD
3 ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... FAST_FOOD
4 ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... FAST_FOOD
... ... ...
8954781 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... BAKERY
8954782 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... BAKERY
8954783 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... BAKERY
8954784 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... BAKERY
8954785 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... BAKERY
city country start_of_day_offset bill_paid_at_local \
0 Albuquerque US 00:00:00 2024-07-19 11:38:03
1 Albuquerque US 00:00:00 2024-07-19 12:05:58
2 Albuquerque US 00:00:00 2024-07-19 12:35:30
3 Albuquerque US 00:00:00 2024-07-19 16:37:06
4 Albuquerque US 00:00:00 2024-07-20 15:31:44
... ... ... ... ...
8954781 Ottawa CA 00:00:00 2024-11-13 15:58:56
8954782 Ottawa CA 00:00:00 2024-11-13 17:01:12
8954783 Ottawa CA 00:00:00 2024-11-14 10:37:29
8954784 Ottawa CA 00:00:00 2024-11-24 11:45:37
8954785 Ottawa CA 00:00:00 2024-11-24 11:43:58
bill_total_billed bill_total_discount_item_level \
0 13.43 0.0
1 12.89 0.0
2 21.02 0.0
3 18.28 0.0
4 5.37 0.0
... ... ...
8954781 10.91 0.0
8954782 7.41 0.0
8954783 11.30 0.0
8954784 3.94 0.0
8954785 5.59 0.0
bill_total_gratuity bill_total_net ... order_duration_seconds \
0 0.0 12.48 ... 849
1 0.0 11.98 ... 767
2 0.0 19.53 ... 549
3 0.0 16.98 ... 71
4 0.0 4.99 ... 29
... ... ... ... ...
8954781 0.0 10.75 ... 41
8954782 0.0 7.25 ... 125
8954783 0.0 10.00 ... 4054
8954784 0.0 3.75 ... 99
8954785 0.0 4.95 ... 3788
order_seated_at_local order_closed_at_local \
0 2024-07-19 11:23:54 2024-07-19 11:38:03
1 2024-07-19 11:53:11 2024-07-19 12:05:58
2 2024-07-19 12:26:21 2024-07-19 12:35:30
3 2024-07-19 16:35:55 2024-07-19 16:37:06
4 2024-07-20 15:31:15 2024-07-20 15:31:44
... ... ...
8954781 2024-11-13 15:58:15 2024-11-13 15:58:56
8954782 2024-11-13 16:59:07 2024-11-13 17:01:12
8954783 2024-11-14 09:29:55 2024-11-14 10:37:29
8954784 2024-11-24 11:43:58 2024-11-24 11:45:37
8954785 2024-11-24 10:40:50 2024-11-24 11:43:58
order_take_out_type_label \
0 dinein
1 dinein
2 dinein
3 dinein
4 dinein
... ...
8954781 dinein
8954782 dinein
8954783 dinein
8954784 dinein
8954785 dinein
order_uuid payment_amount \
0 240719112354~16D0457E-3426-4D27-821B-BACFFA2D4C67 13.43
1 240719115311~B69E1B72-B737-4A4A-9544-0DAD947F7326 12.89
2 240719122621~42A7592C-8821-4E85-A8FE-C72F45D23048 21.02
3 240719163555~E72AAD91-2234-4516-888F-556332409A0B 18.28
4 240720153115~9AD99B33-3CA2-4BE4-A093-D11F9A30B3ED 5.37
... ... ...
8954781 241113155815~6B073DF0-5868-4807-8E22-41FB8696FCD7 11.41
8954782 241113165907~CD9BC7A5-7075-4297-AC77-6792D5BE2D66 8.41
8954783 241114092955~DE58341D-94F1-4739-9C41-874E61605F0F 11.30
8954784 241124114358~1104EA12-8F57-40A8-8C87-D54E02AB56EC 3.94
8954785 241124104050~6FAB8061-3EB7-4148-A4F1-747CD73D2FAD 5.59
payment_count payment_total_tip sales_revenue_with_tax \
0 1 0.0 13.43
1 1 0.0 12.89
2 1 0.0 21.02
3 1 0.0 18.28
4 1 0.0 5.37
... ... ... ...
8954781 1 0.5 10.91
8954782 1 1.0 7.41
8954783 1 0.0 11.30
8954784 1 0.0 3.94
8954785 1 0.0 5.59
waiter_uuid
0 c29c030d-ff51-46be-96f0-d7e00ff56a96
1 c29c030d-ff51-46be-96f0-d7e00ff56a96
2 c29c030d-ff51-46be-96f0-d7e00ff56a96
3 c29c030d-ff51-46be-96f0-d7e00ff56a96
4 c29c030d-ff51-46be-96f0-d7e00ff56a96
... ...
8954781 220225161350~BA623371-965C-4488-99AE-4C53D7440244
8954782 220225161233~80DE5EF4-8AE1-450B-AC0A-3C32DECE1A33
8954783 190405092446~8646FE39-600C-4318-BEA7-0A31D4082914
8954784 220225161233~80DE5EF4-8AE1-450B-AC0A-3C32DECE1A33
8954785 220225161233~80DE5EF4-8AE1-450B-AC0A-3C32DECE1A33
[8954786 rows x 24 columns]
new_df["hour"] = new_df["bill_paid_at_local"].dt.hour # Extract hour of the day
new_df["day_of_week"] = new_df["bill_paid_at_local"].dt.day_name() # Extract weekday
new_df["business_date"] = pd.to_datetime(new_df["business_date"]) # Ensure correct format
new_df["is_weekend"] = new_df["day_of_week"].isin(["Saturday", "Sunday"])
new_df["order_take_out_type_label"] = new_df["order_take_out_type_label"].fillna("Unknown")
# Filter out unrealistic order durations (e.g., negative or >24h)
new_df = new_df[(new_df["order_duration_seconds"] > 0) &
(new_df["order_duration_seconds"] <= 86400)]
# Group by venue and hour to count orders
busiest_hours = new_df.groupby(["venue_xref_id", "hour"]).agg(
orders=("order_uuid", "count"), # Count number of orders
total_revenue=("bill_total_billed", "sum") # Sum total billed amount
).reset_index()
# Sort by number of orders per venue
busiest_hours = busiest_hours.sort_values(by=["venue_xref_id", "orders"], ascending=[True, False])
plt.figure(figsize=(12, 6))
sns.lineplot(data=busiest_hours, x="hour", y="orders", hue="venue_xref_id", marker="o")
plt.title("Busiest Time Windows by Venue")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="Venue ID", bbox_to_anchor=(1, 1))
plt.grid(True)
plt.show()
# Get the top most visited venues
top_5_venues = busiest_hours.groupby("venue_xref_id")["orders"].sum().nlargest(5).index
# Filter the dataset to only include these top 10 venues
filtered_busiest_hours = busiest_hours[busiest_hours["venue_xref_id"].isin(top_5_venues)]
# Plot the busiest time windows for the top 10 venues
plt.figure(figsize=(12, 6))
sns.lineplot(data=filtered_busiest_hours, x="hour", y="orders", hue="venue_xref_id", marker="o")
plt.title("Busiest Time Windows for Top 5 Venues")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="Venue ID", bbox_to_anchor=(1, 1))
plt.grid(True)
plt.show()
new_city_df = df_bills.merge(df_venues[["venue_xref_id", "city", "concept"]], on="venue_xref_id", how="left")
# Extract time features
new_city_df["hour"] = new_city_df["bill_paid_at_local"].dt.hour
new_city_df["day_of_week"] = new_city_df["bill_paid_at_local"].dt.day_name()
new_city_df["is_weekend"] = new_city_df["day_of_week"].isin(["Saturday", "Sunday"])
concept_trends = new_city_df.groupby(["hour", "concept"]).agg(
orders=("order_uuid", "count"),
total_revenue=("bill_total_billed", "sum")
).reset_index()
plt.figure(figsize=(12, 6))
sns.lineplot(data=concept_trends, x="hour", y="orders", hue="concept", marker="o")
plt.title("Busiest Time Windows by Venue Concept")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="Concept", bbox_to_anchor=(1, 1))
plt.grid(True)
plt.show()
# Dine-in vs. Takeout Comparison by Hour
hourly_trends = new_df.groupby(["business_date", "hour", "venue_xref_id"]).agg(
orders=("order_uuid", "count"),
total_revenue=("bill_total_billed", "sum"),
avg_order_duration=("order_duration_seconds", "mean"),
).reset_index()
order_type_trends = new_df.groupby(["hour", "order_take_out_type_label"]).agg(
orders=("order_uuid", "count")
).reset_index()
plt.figure(figsize=(10, 5))
sns.lineplot(data=order_type_trends, x="hour", y="orders", hue="order_take_out_type_label", marker="o")
plt.title("Dine-in vs. Takeout Order Volume by Hour")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="Order Type")
plt.grid(True)
plt.show()
# Weekday vs. Weekend Analysis
weekend_trends = new_df.groupby(["hour", "is_weekend"]).agg(
orders=("order_uuid", "count")
).reset_index()
plt.figure(figsize=(10, 5))
sns.lineplot(data=weekend_trends, x="hour", y="orders", hue="is_weekend", marker="o")
plt.title("Weekday vs. Weekend Order Volume")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="Weekend", labels=["Weekday", "Weekend"])
plt.grid(True)
plt.show()
#Order Duration Impact Analysis
plt.figure(figsize=(10, 5))
sns.lineplot(data=hourly_trends, x="hour", y="avg_order_duration", marker="o", color="red")
plt.title("Average Order Duration by Hour")
plt.xlabel("Hour of the Day")
plt.ylabel("Average Order Duration (seconds)")
plt.xticks(range(0, 24))
plt.grid(True)
plt.show()
#City-Based Analysis
city_trends = new_city_df.groupby(["hour", "city"]).agg(
orders=("order_uuid", "count"),
total_revenue=("bill_total_billed", "sum")
).reset_index()
plt.figure(figsize=(12, 6))
sns.lineplot(data=city_trends, x="hour", y="orders", hue="city", marker="o")
plt.title("Busiest Time Windows by City")
plt.xlabel("Hour of the Day")
plt.ylabel("Number of Orders")
plt.xticks(range(0, 24))
plt.legend(title="City", bbox_to_anchor=(1, 1))
plt.grid(True)
plt.show()
#Revenue Peaks Analysis
revenue_trends = new_city_df.groupby(["hour"]).agg(
total_revenue=("bill_total_billed", "sum")
).reset_index()
plt.figure(figsize=(10, 5))
sns.lineplot(data=revenue_trends, x="hour", y="total_revenue", marker="o", color="green")
plt.title("Revenue Peaks by Hour")
plt.xlabel("Hour of the Day")
plt.ylabel("Total Revenue ($)")
plt.xticks(range(0, 24))
plt.grid(True)
plt.show()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[39], line 50 47 venue_hourly["cluster"] = kmeans.fit_predict(venue_hourly.iloc[:, 1:]) # Exclude venue_xref_id from clustering 49 # Merge cluster labels back to venues ---> 50 venues_df = venues_df.merge(venue_hourly[["venue_xref_id", "cluster"]], on="venue_xref_id", how="left") 52 # Visualize cluster distribution 53 plt.figure(figsize=(8, 5)) NameError: name 'venues_df' is not defined
# Group by venue and count the number of unique orders
top_venues = new_df.groupby("venue_xref_id")["order_uuid"].nunique().reset_index()
# Sort in descending order and select the top 10
top_venues = top_venues.sort_values(by="order_uuid", ascending=False).head(10)
# Rename columns for clarity
top_venues.columns = ["venue_xref_id", "total_orders"]
print(top_venues)
plt.figure(figsize=(10, 5))
sns.barplot(data=top_venues, x="venue_xref_id", y="total_orders", palette="viridis")
plt.title("Top 10 Most Visited Venues")
plt.xlabel("Venue ID")
plt.ylabel("Number of Orders")
plt.xticks(rotation=45)
plt.show()
venue_xref_id total_orders 241 5e8a21ead84851c212c2cae58849de4d37bc0babfcab05... 85961 278 6c4ddbba39d1529689be33c4769223b1194ba2dcb9e20f... 79705 419 a7de7d703442f619ab305b7752fd215d85b28bad1dba9f... 78144 68 1fba23fbdd499646399a75417304d971f5fdea72bce5ba... 74945 266 68e559beb80a2ae3cbdbf07043ebf35fbbccd85494216a... 73329 238 5dc459c71f4739c3dbd5872b4d442295864cf5715f704b... 72663 373 92128c7fc22f3f685e9914450a0bd855e258aafff93516... 67946 273 6afc5202235f3b742e6482c94536ab8462f6022054eccb... 66100 116 3154c9cc4526142b6c75f88dc9137ab19ca25643a9e887... 64263 244 5ece4507d7aec26fdcca66082fcc77457f79cea602c1a8... 63782
C:\Users\Shami-uz Zaman\AppData\Local\Temp\ipykernel_17532\4030445530.py:13: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=top_venues, x="venue_xref_id", y="total_orders", palette="viridis")
# Define busy hours (e.g., peak times 6 PM - 9 PM)
busy_hours = [18, 19, 20]
# Filter data for busy hours
busy_bills = new_df[new_df["bill_paid_at_local"].dt.hour.isin(busy_hours)]
# Group by venue and count unique orders
top_busy_venues = busy_bills.groupby("venue_xref_id")["order_uuid"].nunique().reset_index()
# Sort in descending order and select the top 10
top_busy_venues = top_busy_venues.sort_values(by="order_uuid", ascending=False).head(10)
# Rename columns for clarity
top_busy_venues.columns = ["venue_xref_id", "total_orders"]
print(top_busy_venues)
plt.figure(figsize=(10, 5))
sns.barplot(data=top_busy_venues, x="venue_xref_id", y="total_orders", palette="coolwarm")
plt.title("Top 10 Most Visited Venues During Peak Hours (6 PM - 9 PM)")
plt.xlabel("Venue ID")
plt.ylabel("Number of Orders")
plt.xticks(rotation=45)
plt.show()
venue_xref_id total_orders 395 a7de7d703442f619ab305b7752fd215d85b28bad1dba9f... 27127 88 2a1ff30ab693f59dea389a4b12b3de2a14bcbd09aa65e3... 20386 146 3d88429932ded020d0982da1d6dfe3014d6101105493e9... 18435 185 4c0bc5f6bf2386a758dbf37fe805120a1c51be229565c2... 17969 484 d6840e8b800326e509cc7101f06a5167f95bf64ad4f0de... 16387 535 ef3dd3aa8981b6626ff060397fa9086097152b09f0bb5e... 16183 428 bab90d5522cf2e07bd93c20803edd0566dcccb085957d4... 16131 366 9a19e78a140cac85f15f3cb84262a3d83c161737e04d63... 16121 139 3b50a30e79b576bfeb406fce7be10c3d93929d440319bf... 16102 66 1fc4ef37566db934d64af787adc20b3d1bceb44aefdff4... 15655
C:\Users\Shami-uz Zaman\AppData\Local\Temp\ipykernel_17532\4040678975.py:19: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=top_busy_venues, x="venue_xref_id", y="total_orders", palette="coolwarm")
bill_size_by_order_type = new_df.groupby("order_take_out_type_label").agg(
avg_bill_size=("bill_total_billed", "mean"),
avg_tip=("payment_total_tip", "mean"),
order_count=("bill_uuid", "count")
).reset_index()
bill_size_by_order_type = bill_size_by_order_type.sort_values(by="avg_bill_size", ascending=False)
plt.figure(figsize=(10, 5))
sns.barplot(data=bill_size_by_order_type, x="order_take_out_type_label", y="avg_bill_size")
plt.title("Average Bill Size by Order Type")
plt.xlabel("Order Type")
plt.ylabel("Average Bill Size ($)")
plt.xticks(rotation=45)
plt.grid(True)
plt.show()
plt.figure(figsize=(10, 5))
sns.barplot(data=bill_size_by_order_type, x="order_take_out_type_label", y="avg_tip")
plt.title("Average Tip Amount by Order Type")
plt.xlabel("Order Type")
plt.ylabel("Average Tip Amount ($)")
plt.xticks(rotation=45)
plt.grid(True)
plt.show()
if "order_duration_seconds" in new_df.columns:
duration_analysis = new_df.groupby("order_take_out_type_label").agg(
avg_order_duration=("order_duration_seconds", "mean")
).reset_index()
plt.figure(figsize=(10, 5))
sns.barplot(data=duration_analysis, x="order_take_out_type_label", y="avg_order_duration")
plt.title("Average Order Duration by Order Type")
plt.xlabel("Order Type")
plt.ylabel("Average Order Duration (seconds)")
plt.xticks(rotation=45)
plt.grid(True)
plt.show()
# We use sales_revenue_with_tax as our target for forecasting
daily_sales = new_df.groupby(["business_date", "venue_xref_id"]).agg(
total_sales=("sales_revenue_with_tax", "sum")
).reset_index()
# Prepare the data for Prophet:
# Rename columns: ds for date, y for target.
daily_sales["ds"] = pd.to_datetime(daily_sales["business_date"])
daily_sales["y"] = daily_sales["total_sales"]
# Create additional time features (regressor for Prophet)
daily_sales["day_of_week"] = daily_sales["ds"].dt.day_name()
daily_sales["is_weekend"] = daily_sales["day_of_week"].isin(["Saturday", "Sunday"]).astype(int)
# Sort by date for each venue
daily_sales.sort_values("ds", inplace=True)
# ----------------------------
# 3. Forecasting with Prophet (Per Venue)
# ----------------------------
prophet_forecasts = []
# Loop through each unique venue to build a model and forecast 30 days ahead
for venue in daily_sales["venue_xref_id"].unique():
# Select the data for this venue and only keep needed columns
venue_data = daily_sales[daily_sales["venue_xref_id"] == venue][["ds", "y", "is_weekend"]].copy()
# Check for a minimum number of records (e.g., at least 2)
if venue_data.shape[0] < 2:
print(f"Not enough data for venue {venue}. Skipping Prophet model.")
continue
# Initialize Prophet and add the extra regressor
m = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=False)
m.add_regressor("is_weekend")
try:
m.fit(venue_data)
except Exception as e:
print(f"Error fitting Prophet model for venue {venue}: {e}")
continue
# Create future dataframe (forecast next 30 days)
future = m.make_future_dataframe(periods=30)
# Ensure the regressor is present in future periods
future["is_weekend"] = future["ds"].dt.day_name().isin(["Saturday", "Sunday"]).astype(int)
# Generate the forecast
forecast = m.predict(future)
forecast["venue_xref_id"] = venue # Tag forecast with the venue id
prophet_forecasts.append(forecast)
# Combine forecasts from all venues (if any)
if prophet_forecasts:
forecast_df = pd.concat(prophet_forecasts, ignore_index=True)
else:
forecast_df = pd.DataFrame()
# Plot the forecast vs. actual sales for a sample venue
if not forecast_df.empty:
sample_venue = forecast_df["venue_xref_id"].iloc[0]
sample_actual = daily_sales[daily_sales["venue_xref_id"] == sample_venue]
sample_forecast = forecast_df[forecast_df["venue_xref_id"] == sample_venue]
plt.figure(figsize=(12, 6))
plt.plot(sample_actual["ds"], sample_actual["y"], label="Actual Sales", marker="o")
plt.plot(sample_forecast["ds"], sample_forecast["yhat"], label="Forecasted Sales", linestyle="--", color="red")
plt.title(f"Prophet Forecast vs Actual for Venue {sample_venue}")
plt.xlabel("Date")
plt.ylabel("Sales Revenue with Tax")
plt.legend()
plt.grid(True)
plt.show()
else:
print("No forecasts were generated using Prophet.")
# ----------------------------
# 4. Forecasting with Exponential Smoothing (for a Selected Venue)
# ----------------------------
# Select a venue to forecast using Exponential Smoothing (adjust venue_id as needed)
venue_id = "V001" # Replace with a valid venue_xref_id from your data
venue_data_es = daily_sales[daily_sales["venue_xref_id"] == venue_id].copy()
# Check that there are enough days to capture seasonality (e.g., at least 14 days for weekly seasonality)
if len(venue_data_es) < 14:
print(f"Not enough data for Exponential Smoothing forecast for venue {venue_id}.")
else:
venue_data_es.sort_values("ds", inplace=True)
try:
# Define the model with additive trend and seasonal components (seasonal period = 7 for a weekly cycle)
es_model = ExponentialSmoothing(
venue_data_es["y"],
trend="add",
seasonal="add",
seasonal_periods=7
)
es_fit = es_model.fit()
forecast_period = 30 # Forecast next 30 days
es_forecast = es_fit.forecast(steps=forecast_period)
# Create a corresponding date range for the forecast
future_dates_es = pd.date_range(start=venue_data_es["ds"].max() + pd.Timedelta(days=1), periods=forecast_period)
es_forecast_df = pd.DataFrame({"ds": future_dates_es, "yhat": es_forecast})
plt.figure(figsize=(12, 6))
plt.plot(venue_data_es["ds"], venue_data_es["y"], label="Actual Sales", marker="o")
plt.plot(es_forecast_df["ds"], es_forecast_df["yhat"], label="ES Forecast", marker="o", linestyle="--", color="red")
plt.title(f"Exponential Smoothing Forecast for Venue {venue_id}")
plt.xlabel("Date")
plt.ylabel("Sales Revenue with Tax")
plt.legend()
plt.grid(True)
plt.show()
except Exception as e:
print(f"Error in Exponential Smoothing forecast for venue {venue_id}: {e}")
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Not enough data for Exponential Smoothing forecast for venue V001.
#bills_df = pd.read_csv("bills.csv", parse_dates=["business_date"])
# Fill missing values in the city and concept columns (if necessary)
new_df["city"].fillna("Unknown", inplace=True)
new_df["concept"].fillna("Unknown", inplace=True)
# Aggregate daily sales revenue by venue
daily_sales = new_df.groupby(["business_date", "venue_xref_id"]).agg(
total_sales=("sales_revenue_with_tax", "sum")
).reset_index()
# Extract day of the week, month, and other seasonal features
daily_sales["day_of_week"] = daily_sales["business_date"].dt.day_name()
daily_sales["month"] = daily_sales["business_date"].dt.month
daily_sales["year"] = daily_sales["business_date"].dt.year
# Let's create lag features (previous day's sales) to capture seasonality
daily_sales["previous_day_sales"] = daily_sales.groupby("venue_xref_id")["total_sales"].shift(1)
# Create a column to identify weekends for seasonality
daily_sales["is_weekend"] = daily_sales["day_of_week"].isin(["Saturday", "Sunday"]).astype(int)
# Handle missing values created due to lag features
daily_sales.fillna(0, inplace=True)
# Show a snapshot of the data
print(daily_sales.head())
business_date venue_xref_id \ 0 2024-07-01 02be6761f02f2291c1fe54c103440f7b9ba65da74dc84f... 1 2024-07-01 02fb2dffd171aaf222786c8ad2692aa4fcd35591c443cb... 2 2024-07-01 03eb6859de0e116c9ab7205692c88a1bcc9974a090bec4... 3 2024-07-01 052dcdcf2185a3e75c360ecb53d43c7d9849f3db9798c1... 4 2024-07-01 0584e63a8a4c5040f307ebfc90a206bf66fa75010c6ffc... total_sales day_of_week month year previous_day_sales is_weekend 0 6892.37 Monday 7 2024 0.0 0 1 2161.45 Monday 7 2024 0.0 0 2 647.68 Monday 7 2024 0.0 0 3 2616.10 Monday 7 2024 0.0 0 4 2149.82 Monday 7 2024 0.0 0
new_df.head()
| venue_xref_id | concept | city | country | start_of_day_offset | bill_paid_at_local | bill_total_billed | bill_total_discount_item_level | bill_total_gratuity | bill_total_net | ... | order_uuid | payment_amount | payment_count | payment_total_tip | sales_revenue_with_tax | waiter_uuid | hour | day_of_week | is_weekend | month | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 | 2024-07-19 11:38:03 | 13.43 | 0.0 | 0.0 | 12.48 | ... | 240719112354~16D0457E-3426-4D27-821B-BACFFA2D4C67 | 13.43 | 1 | 0.0 | 13.43 | c29c030d-ff51-46be-96f0-d7e00ff56a96 | 11 | Friday | False | Jul |
| 1 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 | 2024-07-19 12:05:58 | 12.89 | 0.0 | 0.0 | 11.98 | ... | 240719115311~B69E1B72-B737-4A4A-9544-0DAD947F7326 | 12.89 | 1 | 0.0 | 12.89 | c29c030d-ff51-46be-96f0-d7e00ff56a96 | 12 | Friday | False | Jul |
| 2 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 | 2024-07-19 12:35:30 | 21.02 | 0.0 | 0.0 | 19.53 | ... | 240719122621~42A7592C-8821-4E85-A8FE-C72F45D23048 | 21.02 | 1 | 0.0 | 21.02 | c29c030d-ff51-46be-96f0-d7e00ff56a96 | 12 | Friday | False | Jul |
| 3 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 | 2024-07-19 16:37:06 | 18.28 | 0.0 | 0.0 | 16.98 | ... | 240719163555~E72AAD91-2234-4516-888F-556332409A0B | 18.28 | 1 | 0.0 | 18.28 | c29c030d-ff51-46be-96f0-d7e00ff56a96 | 16 | Friday | False | Jul |
| 4 | ea74268311cfcc47d2b4c38ef08b9dab0117226a9a99a9... | FAST_FOOD | Albuquerque | US | 00:00:00 | 2024-07-20 15:31:44 | 5.37 | 0.0 | 0.0 | 4.99 | ... | 240720153115~9AD99B33-3CA2-4BE4-A093-D11F9A30B3ED | 5.37 | 1 | 0.0 | 5.37 | c29c030d-ff51-46be-96f0-d7e00ff56a96 | 15 | Saturday | True | Jul |
5 rows × 28 columns
daily_stats = new_df.groupby(["venue_xref_id", "business_date"]).agg({
"start_of_day_offset": "first", # Each venue has one offset per day
"bill_total_net": "sum",
"bill_total_billed": "sum",
"payment_total_tip": "sum"
}).reset_index()
correlation_matrix = daily_stats[["start_of_day_offset", "bill_total_net", "bill_total_billed", "payment_total_tip"]].corr()
print("Correlation Matrix:\n", correlation_matrix)
plt.figure(figsize=(12, 5))
sns.scatterplot(data=daily_stats, x="start_of_day_offset", y="bill_total_net", alpha=0.6)
plt.title("Correlation Between Business Day Offset & Total Net Sales")
plt.xlabel("Start of Day Offset (Hours)")
plt.ylabel("Total Net Sales")
plt.grid(True)
plt.show()
plt.figure(figsize=(12, 5))
sns.scatterplot(data=daily_stats, x="start_of_day_offset", y="payment_total_tip", alpha=0.6, color="green")
plt.title("Correlation Between Business Day Offset & Total Tips")
plt.xlabel("Start of Day Offset (Hours)")
plt.ylabel("Total Tips")
plt.grid(True)
plt.show()
offset_analysis = daily_stats.groupby("start_of_day_offset").agg(
"bill_total_net": "mean",
"bill_total_billed": "mean",
"payment_total_tip": "mean"
}).reset_index()
print("Offset Analysis:\n", offset_analysis)
sns.lmplot(data=daily_stats, x="start_of_day_offset", y="bill_total_net", aspect=2)
plt.title("Trend: Start of Day Offset vs Net Sales")
plt.show()
Correlation Matrix:
start_of_day_offset bill_total_net bill_total_billed \
start_of_day_offset 1.000000 0.081607 0.083088
bill_total_net 0.081607 1.000000 0.998704
bill_total_billed 0.083088 0.998704 1.000000
payment_total_tip 0.037656 0.236166 0.236636
payment_total_tip
start_of_day_offset 0.037656
bill_total_net 0.236166
bill_total_billed 0.236636
payment_total_tip 1.000000
Offset Analysis:
start_of_day_offset bill_total_net bill_total_billed payment_total_tip
0 0.0 2796.278145 3025.054168 269.162740
1 1.0 3102.406723 3421.151266 185.405736
2 2.0 4174.454257 4574.497383 557.553320
3 3.0 3839.270718 4144.533865 554.763288
4 4.0 4646.445657 5074.533795 570.310507
5 5.0 6629.631224 7174.722029 776.372978
6 6.0 5033.598495 5429.646057 661.632143
7 7.0 3913.353986 4295.245593 499.928153
8 8.0 4944.943745 5389.111527 637.043413
9 9.0 3409.817502 3734.566906 303.158698
10 10.0 2780.326819 3001.177597 405.164317
11 11.0 1963.523548 2146.474872 167.317882
12 12.0 2297.454609 2519.030243 264.322080
13 14.0 2045.619669 2311.198066 366.145083
14 15.0 1171.409623 1268.409488 212.329084
15 16.0 2282.784286 2565.249286 463.484740
16 17.0 3864.430403 4137.520134 459.825369
17 23.0 1021.301853 1080.070971 151.180882
new_df_waiter = new_df.dropna(subset=["waiter_uuid", "bill_total_billed", "payment_total_tip"]).copy()
new_df_waiter["tip_percentage"] = (new_df_waiter["payment_total_tip"] / new_df_waiter["bill_total_billed"]) * 100
waiter_stats = new_df_waiter.groupby("waiter_uuid").agg(
avg_check_size=("bill_total_billed", "mean"),
avg_tip_percentage=("tip_percentage", "mean"),
total_bills=("bill_total_billed", "count")
).reset_index()
top_waiters = waiter_stats.sort_values(by="avg_check_size", ascending=False).head(10)
plt.figure(figsize=(12, 5))
sns.barplot(data=top_waiters, x="waiter_uuid", y="avg_check_size", palette="Blues_r")
plt.xticks(rotation=45)
plt.title("Top 10 Waiters by Average Check Size")
plt.xlabel("Waiter UUID")
plt.ylabel("Average Check Size ($)")
plt.show()
top_tippers = waiter_stats.sort_values(by="avg_tip_percentage", ascending=False).head(10)
plt.figure(figsize=(12, 5))
sns.barplot(data=top_tippers, x="waiter_uuid", y="avg_tip_percentage", palette="Greens_r")
plt.xticks(rotation=45)
plt.title("Top 10 Waiters by Average Tip Percentage")
plt.xlabel("Waiter UUID")
plt.ylabel("Average Tip Percentage (%)")
plt.show()
C:\Users\Shami-uz Zaman\AppData\Local\Temp\ipykernel_17532\3004308811.py:12: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=top_waiters, x="waiter_uuid", y="avg_check_size", palette="Blues_r")
C:\Users\Shami-uz Zaman\AppData\Local\Temp\ipykernel_17532\3004308811.py:21: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=top_tippers, x="waiter_uuid", y="avg_tip_percentage", palette="Greens_r")
# (For demonstration, we simulate weather data.)
unique_dates = pd.to_datetime(new_df["business_date"].dropna().unique())
cities = new_df["city"].dropna().unique()
# Create a weather dataframe with every combination of date and city
weather_data = pd.DataFrame([(date, city) for date in unique_dates for city in cities],
columns=["business_date", "city"])
np.random.seed(42)
weather_data["temperature"] = np.random.uniform(0, 35, len(weather_data)) # in Celsius
weather_data["precipitation"] = np.random.uniform(0, 10, len(weather_data)) # in mm
# Aggregate daily sales by city and business_date
daily_sales_city = new_df.groupby(["business_date", "city"]).agg(
total_billed=("bill_total_billed", "sum"),
total_orders=("order_uuid", "count")
).reset_index()
# Merge weather data with daily sales
weather_sales = pd.merge(daily_sales_city, weather_data, on=["business_date", "city"], how="left")
# Visualize: Temperature vs Total Billed
plt.figure(figsize=(12, 5))
sns.scatterplot(data=weather_sales, x="temperature", y="total_billed", hue="city")
plt.title("Temperature vs Total Billed Amount")
plt.xlabel("Temperature (°C)")
plt.ylabel("Total Billed ($)")
plt.grid(True)
plt.show()
# Visualize: Precipitation vs Total Orders
plt.figure(figsize=(12, 5))
sns.scatterplot(data=weather_sales, x="precipitation", y="total_orders", hue="city")
plt.title("Precipitation vs Total Orders")
plt.xlabel("Precipitation (mm)")
plt.ylabel("Total Orders")
plt.grid(True)
plt.show()
# Heatmap of orders by hour and day-of-week
pivot_table = new_df.pivot_table(index=new_df["bill_paid_at_local"].dt.hour,
columns=new_df["bill_paid_at_local"].dt.day_name(),
values="order_uuid", aggfunc="count")
plt.figure(figsize=(10, 6))
sns.heatmap(pivot_table, cmap="coolwarm", annot=True, fmt=".0f", linewidths=0.5)
plt.title("Order Volume by Hour & Day of the Week")
plt.xlabel("Day of the Week")
plt.ylabel("Hour of the Day")
plt.show()
# Day-of-Week Trends for Average Bill Size
new_df["month"] = new_df["bill_paid_at_local"].dt.strftime("%b")
day_of_week_trends = new_df.groupby(["day_of_week", "order_take_out_type_label"]).agg(
avg_bill_size=("bill_total_billed", "mean"),
avg_tip=("payment_total_tip", "mean"),
order_count=("bill_uuid", "count")
).reset_index()
day_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
day_of_week_trends["day_of_week"] = pd.Categorical(day_of_week_trends["day_of_week"],
categories=day_order, ordered=True)
plt.figure(figsize=(12, 6))
sns.lineplot(data=day_of_week_trends, x="day_of_week", y="avg_bill_size",
hue="order_take_out_type_label", marker="o")
plt.title("Average Bill Size by Order Type & Day of the Week")
plt.xlabel("Day of the Week")
plt.ylabel("Average Bill Size ($)")
plt.xticks(rotation=45)
plt.legend(title="Order Type")
plt.grid(True)
plt.show()
# Monthly Trends for Average Bill Size
monthly_trends = new_df.groupby(["month", "order_take_out_type_label"]).agg(
avg_bill_size=("bill_total_billed", "mean"),
avg_tip=("payment_total_tip", "mean"),
order_count=("bill_uuid", "count")
).reset_index()
month_order = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
monthly_trends["month"] = pd.Categorical(monthly_trends["month"], categories=month_order, ordered=True)
plt.figure(figsize=(12, 6))
sns.lineplot(data=monthly_trends, x="month", y="avg_bill_size",
hue="order_take_out_type_label", marker="o")
plt.title("Seasonality: Average Bill Size by Order Type & Month")
plt.xlabel("Month")
plt.ylabel("Average Bill Size ($)")
plt.xticks(rotation=45)
plt.legend(title="Order Type")
plt.grid(True)
plt.show()
# Tip trends by day-of-week
plt.figure(figsize=(12, 6))
sns.lineplot(data=day_of_week_trends, x="day_of_week", y="avg_tip",
hue="order_take_out_type_label", marker="o")
plt.title("Average Tip Amount by Order Type & Day of the Week")
plt.xlabel("Day of the Week")
plt.ylabel("Average Tip Amount ($)")
plt.xticks(rotation=45)
plt.legend(title="Order Type")
plt.grid(True)
plt.show()
new_df_tip = new_df.dropna(subset=["payment_total_tip", "bill_total_billed"]).copy()
new_df_tip = new_df_tip[new_df_tip["bill_total_billed"] != 0]
new_df_tip["tip_percentage"] = (new_df_tip["payment_total_tip"] / new_df_tip["bill_total_billed"]) * 100
# Aggregate tip metrics by city
city_tips = new_df_tip.groupby("city").agg(
avg_tip_amount=("payment_total_tip", "mean"),
avg_tip_percentage=("tip_percentage", "mean"),
num_bills=("bill_total_billed", "count")
).reset_index()
city_tips = city_tips.sort_values(by="avg_tip_percentage", ascending=False)
print("Top Cities by Tip Percentage:\n", city_tips.head(10))
# Aggregate tip metrics by country
country_tips = new_df_tip.groupby("country").agg(
avg_tip_amount=("payment_total_tip", "mean"),
avg_tip_percentage=("tip_percentage", "mean"),
num_bills=("bill_total_billed", "count")
).reset_index()
country_tips = country_tips.sort_values(by="avg_tip_percentage", ascending=False)
print("Top Countries by Tip Percentage:\n", country_tips.head(10))
plt.figure(figsize=(10, 5))
sns.barplot(data=country_tips, x="country", y="avg_tip_percentage")
plt.xticks(rotation=45, ha="right")
plt.title("Average Tip Percentage by Country")
plt.xlabel("Country")
plt.ylabel("Average Tip Percentage (%)")
plt.grid(True)
plt.show()
# Group by venue concept and city
venue_tips = new_df_tip.groupby(["concept", "city"]).agg(
avg_tip_percentage=("tip_percentage", "mean"),
num_bills=("bill_total_billed", "count")
).reset_index()
venue_tips = venue_tips.sort_values(by="avg_tip_percentage", ascending=False)
g = sns.catplot(
data=venue_tips,
x="concept",
y="avg_tip_percentage",
col="city",
kind="bar",
col_wrap=3,
height=4,
aspect=1.2
)
g.set_xticklabels(rotation=45, ha="right")
plt.tight_layout()
plt.show()
# Convert categorical columns to numeric codes and compute correlations
new_df_tip["city_code"] = new_df_tip["city"].astype("category").cat.codes
new_df_tip["country_code"] = new_df_tip["country"].astype("category").cat.codes
new_df_tip["order_type_code"] = new_df_tip["order_take_out_type_label"].astype("category").cat.codes
corr_df = new_df_tip[["tip_percentage", "city_code", "country_code", "order_type_code", "bill_total_billed"]]
correlation_matrix_tip = corr_df.corr()
print("Correlation Matrix for Tip Culture:\n", correlation_matrix_tip)
Top Cities by Tip Percentage:
city avg_tip_amount avg_tip_percentage num_bills
61 Cedartown 5.228590 1456.663457 6463
212 Naples 6.059746 1299.714426 6256
283 Roseville 21.156823 809.060954 6273
164 Kimberley 29.433145 677.266580 6317
94 Delta 13.003250 547.072946 6541
370 West Kelowna 11.649260 541.922473 18608
270 Regina 5.768081 468.712812 5989
18 Austin 22.050260 410.097790 6298
291 Santa Ana 17.537565 334.041265 16472
93 Delta 8.765065 282.278452 19910
Top Countries by Tip Percentage:
country avg_tip_amount avg_tip_percentage num_bills
0 CA 3.449548 61.054201 5498931
1 US 4.303625 31.720924 3179768
Correlation Matrix for Tip Culture:
tip_percentage city_code country_code order_type_code \
tip_percentage 1.000000 0.000294 -0.002417 -0.002566
city_code 0.000294 1.000000 -0.201229 0.001621
country_code -0.002417 -0.201229 1.000000 0.072175
order_type_code -0.002566 0.001621 0.072175 1.000000
bill_total_billed -0.003196 -0.001270 0.008165 -0.016711
bill_total_billed
tip_percentage -0.003196
city_code -0.001270
country_code 0.008165
order_type_code -0.016711
bill_total_billed 1.000000
unique_dates = pd.to_datetime(new_df["business_date"].dropna().unique())
inflation_data = pd.DataFrame({"business_date": unique_dates})
np.random.seed(42)
inflation_data["inflation_rate"] = np.random.uniform(0, 0.05, len(inflation_data)) # Simulated rate
daily_sales_infl = new_df.groupby(["business_date", "city"]).agg(
total_billed=("bill_total_billed", "sum"),
total_net=("bill_total_net", "sum"),
total_sales_with_tax=("sales_revenue_with_tax", "sum")
).reset_index()
inflation_merge = pd.merge(daily_sales_infl, inflation_data, on="business_date", how="left")
plt.figure(figsize=(12, 5))
sns.scatterplot(data=inflation_merge, x="inflation_rate", y="total_billed", hue="city")
plt.title("Inflation Rate vs Total Billed Amount")
plt.xlabel("Inflation Rate")
plt.ylabel("Total Billed ($)")
plt.grid(True)
plt.show()
plt.figure(figsize=(12, 5))
sns.scatterplot(data=inflation_merge, x="inflation_rate", y="total_sales_with_tax", hue="city")
plt.title("Inflation Rate vs Sales Revenue (with Tax)")
plt.xlabel("Inflation Rate")
plt.ylabel("Sales Revenue with Tax ($)")
plt.grid(True)
plt.show()
